18 citations · 18 across the 5 of their papers we have counts for
4 papers · 1 filter
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition
Yuzhao Mao, Di Lu, Xiaojie Wang +1
This paper concentrates on the understanding of interlocutors' emotions evoked in conversational utterances. Previous studies in this literature mainly focus on more accurate emoti…
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification
Guang Liu, Hailong Huang, Yuzhao Mao +3
Data augmentation (DA) aims to generate constrained and diversified data to improve classifiers in Low-Resource Classification (LRC). Previous studies mostly use a fine-tuned Langu…
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup
Guang Liu, Yuzhao Mao, Hailong Huang +2
Mixup is a recent regularizer for current deep classification networks. Through training a neural network on convex combinations of pairs of examples and their labels, it imposes l…
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation
Yuzhao Mao, Qi Sun, Guang Liu +4
Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems. Existing studies on ERC primarily focus on summarizing the context informatio…